Health care provider sleep deprivation has proven to be a substantial patient safety hazard. The Institute of Medicine estimates that as many as 98,000 Americans may die each year from medical errors. According to interns'own reports in the AHRQ-funded Harvard Work Hours, Health, and Safety Study, thousands of preventable deaths may be attributable to the long work hours of physicians-in- training. Nurses have similarly been shown to make many more medical errors when working extended hours. While the contribution of provider sleep deprivation to medical error is well-demonstrated, evidence- based solutions to traditional scheduling practices are lacking. Tools to help managers and program directors identify high risk schedules and adapt evidence-based recommendations for use in each distinct clinical environment are needed. We therefore propose: 1. To extend a mathematical model of performance and alertness based on principals of sleep and circadian medicine to develop a time-varying schedule performance index (SPI) that will quantify a measure of risk due to health care providers'work schedules. 2. To test the correlation between decrements predicted using the SPI and empiric performance data obtained from interns in the AHRQ-funded Intern Sleep and Patient Safety Study. 3. To pilot test the SPI's ability to predict the measured risk previously established empirically in the Harvard Work Hours, Health, and Safety Dataset. Initial development of the SPI in these populations will be the first step in an effort to develop, validate, and test the use of schedule modeling software to identify and reduce fatigue-related performance decrements across healthcare professions. In anticipation of this goal, we will engage the participation of a multi-disciplinary advisory group to guide us in the implementation and dissemination of the index to improve the schedules of nurses, physicians-in-training, practicing physicians, physicians'assistants, pharmacists, and other healthcare workers. PUBLIC HEALTH RELEVANCE: The traditional long work hours of healthcare providers lead to many preventable medical errors, and injuries to the providers themselves. We propose developing a scientifically-based mathematical "risk index" to study healthcare workers'schedules. This index would allow healthcare managers to quantify the risk associated with current work schedules and design safer alternatives.